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This paper attempts to propose a hybrid adaptive cerebellar model articulation controller (CMAC) sliding mode control (SMC; called HAC-SMC) with a supervisory controller for a class of nonlinear system, in which the HAC composed of a direct adaptive CMAC and an indirect adaptive CMAC control is performed as the SMC.
One is based on pure CMAC and another one based on both the CMAC and PID.
The ACNC system is composed of an adaptive CMAC and a compensation controller.
It resembles a neural structure that derived from the Albus CMAC and Takagi Sugeno Kang parametric fuzzy inference systems.
Adaptive CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between adaptive CMAC and ideal controller.
The purpose of this study is to integrate the cerebellar model articulation controller (CMAC) and the sliding mode control (SMC) into the aircraft landing system.
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ESD conducted the bioinformatics and molecular analyses, and CMAC conceived and coordinated the project; both authors wrote the manuscript.
AG carried out the molecular and genetic studies, AG, FRL and EL did the bioinformatic analyses, CV and CMAC designed and coordinated the study.
In general, the community did not distinguish between the CBMRR project and other CMAC activities and, accordingly, their expectations of the CBMRR project were not distinguishable.
The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance.
CMAC conceived and supervised the project, participated in data interpretation and wrote the manuscript.
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